1 PRESENTED BY Kanav Mathur 2427030777 School of Computer Science and Engineering Department of Computer Science and Engineering Federated Spatio-Temporal Graph Network SUPERVISED BY Dr. Shishir Singh Chauhan

2 03 04 Introduction 05 06 Literature Review 07 Problem Statement 08 Objectives 09 Proposed Solution 10 11 12 Outline

3 Introduction 03 Urban traffic congestion is a significant global challenge Real-time traffic forecasting enhances: • Route planning • Signal Control • Emergency Response Traffic data obtained from a network of road sensors Conventional deep learning models rely on centralized data Real-world scenarios are geographically distributed

4 Federated Learning for Traffic Traffic sensors are geographically divided Data ownership and privacy issues Communication limitations between regions Centralized models can: • Fail to generalize well • Disregard regional heterogeneity Require: A distributed learning framework that: • Respects data privacy • Captures spatial-temporal patterns • Generalizes well to new city regions This Photo by Unknown Author is licensed under CC BY-SA

5 Spatio-Temporal Traffic Models Graph-based Models: STGCN (Spatio-Temporal Graph Convolutional Networks) DCRNN STGAT (Graph Attention Networks) Key Ideas: Use road graph to model spatial dependency Use convolution or RNN to model temporal dynamics Use adjacency matrix to represent traffic influence propagation Limitations: Need centralized data access Assume fixed graph structure

6 Federated Learning in Intelligent Systems Federated Learning(FL): Local training on distributed clients Aggregation using FedAvg No raw data sharing Applications: Healthcare Mobile systems IoT networks Gap in Literature: Few studies on federated graph learning for traffic Few studies on inductive generalization to unseen regions

7 Problem Statement How can 1. Different zones in city learn traffic patterns without sharing data but still understand roads causing traffic 2. Global model generalize to unseen regions

8 Objectives Primary Objectives Design centralized STGCN baseline Implement federated traffic learning simulation Test inductive generalization Implement adaptive graph learning Performance Objectives Achieve minimum MAE and RMSE Compare: Centralized vs federated Fixed graph vs adaptive graph Test robustness across regions

9 Proposed Solution Key Components: • Local STGCN per region • Adaptive graph correction module • Federated parameter aggregation • Inductive evaluation on unseen region Core Novelty: • Learn spatial dependencies collaboratively • Preserve regional data privacy

10 Dataset and Preprocessing Dataset: METR-LA 207 traffic sensors 5-minute intervals Road distance-based adjacency Speed values are stored Sensors are pre-ided Preprocessing: Missing value imputation Z-score normalization Sliding window generation (12 12) →

11 Sensor Clustering & Federated Simulation

12 Local STGCN Architecture

13 Learnable Spatial Dependency Modelling Benefits: Captures hidden traffic correlations Models indirect congestion propagation Improves generalization across regions

14 Federated Optimization Procedure Advantages: Privacy preservation Reduced communication overhead Scalable architecture

15 Purpose: Test real-world deployability Evaluate cross-region knowledge transfer Demonstrate inductive capability Generalization to Unseen Region

16 Training Details & Optimization Loss Function Mean Squared Error(MSE) Evaluation Metrics MAE,RMSE Optimizer Adam Learning Rate 0.001 Batch Size 32 Epochs 10-20 per round Activation ReLU Regularization Early stopping Weight sharing across client

17 Result Outcome I aim to achieve drafting the research paper for this model.

18 Thank you